Room Layout Estimation on Mobile Devices
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Automatic Generation of a 3D City Model
UNIVERSITY OF CASTILLA-LA MANCHA ESCUELA SUPERIOR DE INFORMÁTICA COMPUTER ENGINEERING DEGREE DEGREE FINAL PROJECT Automatic generation of a 3D city model David Murcia Pacheco June, 2017 AUTOMATIC GENERATION OF A 3D CITY MODEL Escuela Superior de Informática UNIVERSITY OF CASTILLA-LA MANCHA ESCUELA SUPERIOR DE INFORMÁTICA Information Technology and Systems SPECIFIC TECHNOLOGY OF COMPUTER ENGINEERING DEGREE FINAL PROJECT Automatic generation of a 3D city model Author: David Murcia Pacheco Director: Dr. Félix Jesús Villanueva Molina June, 2017 David Murcia Pacheco Ciudad Real – Spain E-mail: [email protected] Phone No.:+34 625 922 076 c 2017 David Murcia Pacheco Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled "GNU Free Documentation License". i TRIBUNAL: Presidente: Vocal: Secretario: FECHA DE DEFENSA: CALIFICACIÓN: PRESIDENTE VOCAL SECRETARIO Fdo.: Fdo.: Fdo.: ii Abstract HIS document collects all information related to the Degree Final Project (DFP) of Com- T puter Engineering Degree of the student David Murcia Pacheco, tutorized by Dr. Félix Jesús Villanueva Molina. This work has been developed during 2016 and 2017 in the Escuela Superior de Informática (ESI), in Ciudad Real, Spain. It is based in one of the proposed sub- jects by the faculty of this university for this year, called "Generación automática del modelo en 3D de una ciudad". -
Benchmarks on WWW Performance
The Scalability of X3D4 PointProperties: Benchmarks on WWW Performance Yanshen Sun Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Application Nicholas F. Polys, Chair Doug A. Bowman Peter Sforza Aug 14, 2020 Blacksburg, Virginia Keywords: Point Cloud, WebGL, X3DOM, x3d Copyright 2020, Yanshen Sun The Scalability of X3D4 PointProperties: Benchmarks on WWW Performance Yanshen Sun (ABSTRACT) With the development of remote sensing devices, it becomes more and more convenient for individual researchers to acquire high-resolution point cloud data by themselves. There have been plenty of online tools for the researchers to exhibit their work. However, the drawback of existing tools is that they are not flexible enough for the users to create 3D scenes of a mixture of point-based and triangle-based models. X3DOM is a WebGL-based library built on Extensible 3D (X3D) standard, which enables users to create 3D scenes with only a little computer graphics knowledge. Before X3D 4.0 Specification, little attention has been paid to point cloud rendering in X3DOM. PointProperties, an appearance node newly added in X3D 4.0, provides point size attenuation and texture-color mixing effects to point geometries. In this work, we propose an X3DOM implementation of PointProperties. This implementation fulfills not only the features specified in X3D 4.0 documentation, but other shading effects comparable to the effects of triangle-based geometries in X3DOM, as well as other state-of-the-art point cloud visualization tools. -
Seamless Texture Mapping of 3D Point Clouds
Seamless Texture Mapping of 3D Point Clouds Dan Goldberg Mentor: Carl Salvaggio Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology Rochester, NY November 25, 2014 Abstract The two similar, quickly growing fields of computer vision and computer graphics give users the ability to immerse themselves in a realistic computer generated environment by combining the ability create a 3D scene from images and the texture mapping process of computer graphics. The output of a popular computer vision algorithm, structure from motion (obtain a 3D point cloud from images) is incomplete from a computer graphics standpoint. The final product should be a textured mesh. The goal of this project is to make the most aesthetically pleasing output scene. In order to achieve this, auxiliary information from the structure from motion process was used to texture map a meshed 3D structure. 1 Introduction The overall goal of this project is to create a textured 3D computer model from images of an object or scene. This problem combines two different yet similar areas of study. Computer graphics and computer vision are two quickly growing fields that take advantage of the ever-expanding abilities of our computer hardware. Computer vision focuses on a computer capturing and understanding the world. Computer graphics con- centrates on accurately representing and displaying scenes to a human user. In the computer vision field, constructing three-dimensional (3D) data sets from images is becoming more common. Microsoft's Photo- synth (Snavely et al., 2006) is one application which brought attention to the 3D scene reconstruction field. Many structure from motion algorithms are being applied to data sets of images in order to obtain a 3D point cloud (Koenderink and van Doorn, 1991; Mohr et al., 1993; Snavely et al., 2006; Crandall et al., 2011; Weng et al., 2012; Yu and Gallup, 2014; Agisoft, 2014). -
From Google Maps to a Fine-Grained Catalog of Street Trees
From Google Maps to a Fine-Grained Catalog of Street trees Steve Bransona,1, Jan Dirk Wegnerb,1, David Halla, Nico Langb, Konrad Schindlerb, Pietro Peronaa aComputational Vision Laboratory, California Institute of Technology, USA bPhotogrammetry and Remote Sensing, ETH Z¨urich,Switzerland Abstract Up-to-date catalogs of the urban tree population are of importance for municipalities to monitor and improve quality of life in cities. Despite much research on automation of tree mapping, mainly relying on on dedicated airborne LiDAR or hyperspectral campaigns, tree detection and species recognition is still mostly done manually in practice. We present a fully automated tree detection and species recognition pipeline that can process thousands of trees within a few hours using publicly available aerial and street view images of Google MapsTM. These data provide rich information from different viewpoints and at different scales from global tree shapes to bark textures. Our work-flow is built around a supervised classification that automatically learns the most discriminative features from thousands of trees and corresponding, publicly available tree inventory data. In addition, we introduce a change tracker that recognizes changes of individual trees at city-scale, which is essential to keep an urban tree inventory up-to-date. The system takes street-level images of the same tree location at two different times and classifies the type of change (e.g., tree has been removed). Drawing on recent advances in computer vision and machine learning, we apply convolutional neural networks (CNN) for all classification tasks. We propose the following pipeline: download all available panoramas and overhead images of an area of interest, detect trees per image and combine multi-view detections in a probabilistic framework, adding prior knowledge; recognize fine-grained species of detected trees. -
Creating Google Street Views with the Samsung Gear 360 Camera By
Creating Google Street Views with the Samsung Gear 360 Camera1 by Andy Lyons Last modified: November 2016 Google Street View Camera Loan Kit In October 2016, Google loaned a 360-camera kit to IGIS (a statewide technical support program in ANR), for the purposes of exploring how Google’s Street View technology can be useful for research and management at the Hopland Research and Extension Center (and ANR field stations more generally). Street View app and the Samsung Gear 360 2 The Street View app (for both Android and iOS) is what you use to create and upload Street Views. The Samsung Gear 360 is one of about four 360 cameras that the Google Street View app is setup to work with (meaning the app can control the camera pretty seamlessly). Most people use the Street View app only to view off-road street views. You can also view off-road street views in plain old Google Maps, but the Street View app makes it a little easier to find user generated content. If you have a VR device such as the Google Cardboard, you can view Street Views in 3 cardboard mode . In terms of content creation, the Street View app has three main functions: i) control the camera, ii) process images (which includes stitching them together, blurring faces and license plates, adding locations as needed, and linking nearby photos), and iii) upload the images to Google Street View (after which they’ll be available in Google Maps immediately). The Samsung Gear can also record 360 video. For that you need the Samsung Gear 360 Manager (or another app). -
Google Camera Apk Download for Android 9.0
Google camera apk download for android 9.0 Continue Although the Google Play Store has over a million apps that you can install on an Android device, the market sometimes removes popular software from its catalog such as Grooveshark Mobile and Adobe Flash Player. However, you don't have to download apps only from the official market; You can set up your device to download installation packages or APK files from elsewhere. To download a package from an email app and install it on Android, you need to download and use a third-party program. Open The Settings from the app screen or notification bar, and then tap Security. Scroll down to the device's administration and then check the Unknown Sources option. Download the attachment from your email app or mobile browser, and then open the Google Play Store from the Home or Apps screen. Search and then install Apk Installer Graphilos Studio from the Play Store. Open the app to complete the installation, and then review the folder containing the downloaded package. Select the APK file from the file manager, and then tap the Package Installer to start the setup. Follow the tips on the screen to install APK content on your smartphone. Open The Settings from the app screen or notification bar, and then tap Security. Scroll down to the device's administration and then check the Unknown Sources option. Download the attachment from your email app or mobile browser, and then open the Google Play Store from the Home or Apps screen. Search and then install Apk Installer by Array Infotech from the Play Store. -
Progress, Privacy, and Google Jamuna D
Brooklyn Law Review Volume 74 | Issue 1 Article 7 2008 A Computer with a View: Progress, Privacy, and Google Jamuna D. Kelley Follow this and additional works at: https://brooklynworks.brooklaw.edu/blr Recommended Citation Jamuna D. Kelley, A Computer with a View: Progress, Privacy, and Google, 74 Brook. L. Rev. (2008). Available at: https://brooklynworks.brooklaw.edu/blr/vol74/iss1/7 This Note is brought to you for free and open access by the Law Journals at BrooklynWorks. It has been accepted for inclusion in Brooklyn Law Review by an authorized editor of BrooklynWorks. A Computer with a View PROGRESS, PRIVACY, AND GOOGLE INTRODUCTION [Tihe existing law affords a principle which may be invoked to protect the privacy of the individual from invasion either by the too enterprising press, the photographer .... [or] any other modern device for recording or reproducing scenes .... [Is it the case that] any individual, by appearing upon the public highway, or in any other public place, makes his appearance public, so that any one may take and publish a picture of him as he is at the time[?] What if an utterly obscure citizen, reeling along drunk on the main street, is snapped by an 2enterprising reporter, and the picture given to the world? Is his privacy invaded? The authors of the quotations above, Samuel Warren, Louis Brandeis, and William Prosser, were not referring to the Internet when they described the increasing invasion of modem devices into personal privacy, but their words are still poignant for many citizens of a world in which novel technology seems to sprout silently, rapidly, and endlessly. -
Augmented Reality in Cultural Heritages
AR in Cultural Historical Museum Anil Ghimire Master’s thesis in Software Engineering at Department of Computing, Mathematics and Physics, Bergen University College Department of Informatics, University of Bergen June 2019 Abstract i Acknowledgements I offer my sinceriest gratitude to my supervisors (Harald, Atle, Daniel) , new media center HVL and the bachelor groups who helped in designing the model for the project. ii Contents Abstracti Acknowledgements ii List Of Figures vi List Of Tables vii Acronyms viii Glossary ix 1 Introduction1 1.1 A word about Centre for New Media .............. 1 1.2 Motivation............................. 2 1.3 Thesis goals and Research Questions .............. 2 1.4 Related Works........................... 3 1.5 Report Structure ......................... 3 2 Background4 2.1 Museums and Technology .................... 4 2.1.1 Virtual Reality (VR)................... 5 2.1.2 Other Technologies.................... 5 2.1.3 Augmented Reality (AR) ................ 5 2.1.4AR in museums...................... 5 3 Augmented Reality : An Overview8 3.1 What isAR? ........................... 8 3.2 History ofAR........................... 9 iii 3.3 Key Technologies......................... 10 3.3.1 Display Technology.................... 10 3.3.2 Tracking and Registeration Technology......... 11 3.3.3 Registeration ....................... 13 3.3.4 Calibration ........................ 13 3.4 HowAR system works?...................... 13 3.5 Current Uses of AR........................ 14 3.6 AR frameworks.......................... 14 3.6.1 ARKit........................... 15 3.6.2 ARCore .......................... 15 3.6.3 Vuforia........................... 16 3.6.4 Comparision........................ 16 4 ARCore: An Overview 18 4.1 ARCore and Project Tango ................... 18 4.2 Development Environments ................... 19 4.2.1 Android Studio...................... 19 4.2.2 Unreal Engine....................... 19 4.2.3 Unity .......................... -
Objekttracking Für Dynamisches Videoprojektions Mapping
Bachelorthesis Iwer Petersen Using object tracking for dynamic video projection mapping Fakultät Technik und Informatik Faculty of Engineering and Computer Science Studiendepartment Informatik Department of Computer Science Iwer Petersen Using object tracking for dynamic video projection mapping Bachelorthesis submitted within the scope of the Bachelor examination in the degree programme Bachelor of Science Technical Computer Science at the Department of Computer Science of the Faculty of Engineering and Computer Science of the Hamburg University of Applied Science Mentoring examiner: Prof. Dr. Ing. Birgit Wendholt Second expert: Prof. Dr.-Ing. Andreas Meisel Submitted at: January 31, 2013 Iwer Petersen Title of the paper Using object tracking for dynamic video projection mapping Keywords video projection mapping, object tracking, point cloud, 3D Abstract This document presents a way to realize video projection mapping onto moving objects. Therefore a visual 3D tracking method is used to determine the position and orientation of a known object. Via a calibrated projector-camera system, the real object then is augmented with a virtual texture. Iwer Petersen Thema der Arbeit Objekttracking für dynamisches Videoprojektions Mapping Stichworte Video Projektions Mapping, Objektverfolgung, Punktwolke, 3D Kurzzusammenfassung Dieses Dokument präsentiert einen Weg um Video Projektions Mapping auf sich bewegende Objekte zu realisieren. Dafür wird ein visuelles 3D Trackingverfahren verwendet um die Position und Lage eines bekannten Objekts zu bestimmen. Über ein kalibriertes Projektor- Kamera System wird das reale Objekt dann mit einer virtuellen Textur erweitert. Contents 1 Introduction1 1.1 Motivation . .1 1.2 Structure . .3 2 Related Work4 2.1 Corresponding projects . .4 2.1.1 Spatial Augmented Reality . .4 2.1.2 2D Mapping onto people . -
Basic Implementation and Measurements of Plane Detection
Basic Implementation and Measurements of Plane Detection in Point Clouds A project of the 2017 Robotics Course of the School of Information Science and Technology (SIST) of ShanghaiTech University https://robotics.shanghaitech.edu.cn/teaching/robotics2017 Chen Chen Wentao Lv Yuan Yuan School of Information and School of Information and School of Information and Science Technology Science Technology Science Technology [email protected] [email protected] [email protected] ShanghaiTech University ShanghaiTech University ShanghaiTech University Abstract—In practical robotics research, plane detection is an Corsini et al.(2012) is working on dealing with the problem important prerequisite to a wide variety of vision tasks. FARO is of taking random samples over the surface of a 3D mesh another powerful devices to scan the whole environment into describing and evaluating efficient algorithms for generating point cloud. In our project, we are working to apply some algorithms to convert point cloud data to plane mesh, and then different distributions. In this paper, the author we propose path planning based on the plane information. Constrained Poisson-disk sampling, a new Poisson-disk sam- pling scheme for polygonal meshes which can be easily I. Introduction tweaked in order to generate customized set of points such as In practical robotics research, plane detection is an im- importance sampling or distributions with generic geometric portant prerequisite to a wide variety of vision tasks. Plane constraints. detection means to detect the plane information from some Kazhdan and Hoppe(2013) is talking about poisson surface basic disjoint information, for example, the point cloud. In this reconstruction, which can create watertight surfaces from project, we will use point clouds from FARO devices which oriented point sets. -
Point Cloud Subjective Evaluation Methodology Based on Reconstructed Surfaces
Point cloud subjective evaluation methodology based on reconstructed surfaces Evangelos Alexioua, Antonio M. G. Pinheirob, Carlos Duartec, Dragan Matkovi´cd, Emil Dumi´cd, Luis A. da Silva Cruzc, Lovorka Gotal Dmitrovi´cd, Marco V. Bernardob Manuela Pereirab and Touradj Ebrahimia aMultimedia Signal Processing Group, Ecole´ Polytechnique F´ed´eralede Lausanne, Switzerland; bInstitute of Telecommunications, University of Beira Interior, Portugal; cDepartment of Electrical and Computer Engineering, University of Coimbra and Institute of Telecommunications, Portugal; dDepartment of Electrical Engineering, University North, Croatia ABSTRACT Point clouds have been gaining importance as a solution to the problem of efficient representation of 3D geometric and visual information. They are commonly represented by large amounts of data, and compression schemes are important for their manipulation transmission and storing. However, the selection of appropriate compression schemes requires effective quality evaluation. In this work a subjective quality evaluation of point clouds using a surface representation is analyzed. Using a set of point cloud data objects encoded with the popular octree pruning method with different qualities, a subjective evaluation was designed. The point cloud geometry was presented to observers in the form of a movie showing the 3D Poisson reconstructed surface without textural information with the point of view changing in time. Subjective evaluations were performed in three different laboratories. Scores obtained from each test were correlated and no statistical differences were observed. Scores were also correlated with previous subjective tests and a good correlation was obtained when compared with mesh rendering in 2D monitors. Moreover, the results were correlated with state of the art point cloud objective metrics revealing poor correlation. -
MEPP2: a Generic Platform for Processing 3D Meshes and Point Clouds
EUROGRAPHICS 2020/ F. Banterle and A. Wilkie Short Paper MEPP2: a generic platform for processing 3D meshes and point clouds Vincent Vidal, Eric Lombardi , Martial Tola , Florent Dupont and Guillaume Lavoué Université de Lyon, CNRS, LIRIS, Lyon, France Abstract In this paper, we present MEPP2, an open-source C++ software development kit (SDK) for processing and visualizing 3D surface meshes and point clouds. It provides both an application programming interface (API) for creating new processing filters and a graphical user interface (GUI) that facilitates the integration of new filters as plugins. Static and dynamic 3D meshes and point clouds with appearance-related attributes (color, texture information, normal) are supported. The strength of the platform is to be generic programming oriented. It offers an abstraction layer, based on C++ Concepts, that provides interoperability over several third party mesh and point cloud data structures, such as OpenMesh, CGAL, and PCL. Generic code can be run on all data structures implementing the required concepts, which allows for performance and memory footprint comparison. Our platform also permits to create complex processing pipelines gathering idiosyncratic functionalities of the different libraries. We provide examples of such applications. MEPP2 runs on Windows, Linux & Mac OS X and is intended for engineers, researchers, but also students thanks to simple use, facilitated by the proposed architecture and extensive documentation. CCS Concepts • Computing methodologies ! Mesh models; Point-based models; • Software and its engineering ! Software libraries and repositories; 1. Introduction GUI for processing and visualizing 3D surface meshes and point clouds. With the increasing capability of 3D data acquisition devices, mod- Several platforms exist for processing 3D meshes such as Meshlab eling software and graphics processing units, three-dimensional [CCC08] based on VCGlibz, MEPP [LTD12], and libigl [JP∗18].